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Isomorphisms Math 130 Linear Algebra the identity function on B. The usual notation for the function inverse to f is f −1. If f and g are inverse to each other, that is, if g is the inverse of f, g = f −1, then f is the inverse of Isomorphisms g, f = g−1. Thus, (f −1)−1 = f. Math 130 Linear Algebra An important property of bijections is that you D Joyce, Fall 2013 can convert equations involving f to equations in- volving f −1: Frequently in mathematics we look at two alge- −1 braic structures A and B of the same kind and want f(x) = y if and only if x = f (y) to compare them. For instance, we might think they're really the same thing, but they have dif- . ferent names for their elements. That leads to the concept of isomorphism f : A !' B, and we'll talk Isomorphisms of algebraic structures. There about that first. Other times we'll know they're are lots of different kinds of algebraic structures. not the same thing, but there is a relation between We've already studied two of them, namely, fields them, and that will lead to the next concept, ho- and vector spaces. momorphism, f : A ! B. We'll then look as some We'll say two algebraic structures A and B are special homomorphisms such as monomorphisms. isomorphic if they have exactly the same structure, When we have a homomorphism f : A ! A, we'll but their elements may be different. For instance, call it an endomorphism, and when an isomorphism let A be the vector space R[x] of polynomials in f : A !' A, we'll call it an automorphism. We'll the variable x, and let B be the vector space R[y] take each of these variants in turn. of polynomials in y. They're both just polynomials in one variable, it's just that the choice of variable Injections, surjections, and bijections of is different in the two rings. functions between sets. These are words that We're studying vector spaces, so we need a pre- describe certain functions f : A ! B from one set cise definition of isomorphism for them. to another. Definition 1 (Isomorphism of vector spaces). Two An injection, also called a one-to-one function vector spaces V and W over the same field F are is a function that maps distinct elements to dis- isomorphic if there is a bijection T : V ! W which tinct elements, that is, if x 6= y, then f(x) 6= f(y). preserves addition and scalar multiplication, that Equivalently, if f(x) = f(y) then x = y. If A is, for all vectors u and v in V , and all scalars is a subset of B, then there is a natural injection c 2 F , ι : A ! B, called the inclusion function, defined by ι(x) = x. T (u + v) = T (u) + T (v) and T (cv) = cT (v): A surjection, also called an onto function is one that includes all of B in its image, that is, if y 2 B, The correspondence T is called an isomorphism of then there is an x 2 A such that f(x) = y. vector spaces. A bijection, also called a one-to-one correspon- dence, is a function that is simultaneously injective When T : V ! W is an isomorphism we'll write and bijective. Another way to describe a bijection T : V !' W if we want to emphasize that it is an f : A ! B is to say that there is an inverse function isomorphism. When V and W are isomorphic, but g : B ! A so that the composition g ◦ f : A ! A the specific isomorphism is not named, we'll just is the identity function on A while f ◦ g : B ! B is write V ∼= W . 1 ' Of course, the identity function IV : V ! V is Example 4. Consider P3, the vector space of poly- ' an isomorphism. nomials over R of degree 3 or less. Define T : P3 ! 4 3 2 After we introduce linear transformations (which R by T (a1x + a2x + a3x + a4) = (a1; a2; a3; a4). is what homomorphisms of vector spaces are It just associates to a polynomial its 4-tuple of co- called), we'll have another way to describe isomor- efficients starting with the coefficient of x3 and go- phisms. ing down in degree. This T preserves addition and You can prove various properties of vector space scalar multiplication, it is one-to-one, and it is onto. isomorphisms from this definition. (Those statements are easy to verify.) ' 4 Since the structure of vector spaces is defined in This is not the only isomorphism P3 ! R .A terms of addition and scalar multiplication, if T cubic polynomial is determined by its value at any preserves them, it will preserve structure defined in four points. The association f(x) to the 4-tuple terms of them. For instance, T preserves 0, nega- (f(1); f(2); f(3); f(4)) is also an isomorphism. tion, subtraction, and linear transformations. Theorem 5. If T : V ! W is an isomorphism, Theorem 2. If T : V !' W is an isomorphism of then T carries linearly independent sets to linearly vector spaces, then its inverse T −1 : W !' V is also independent sets, spanning sets to spanning sets, an isomorphism. and bases to bases. Proof. Since T is a bijection, T −1 exists as a func- Proof. For the first statement, let S be a set of lin- tion W ! V . We have to show T −1 preserves ad- early independent vectors in V . We'll show that its dition and scalar multiplication. image T (S) is a set of linearly independent vectors in W . If 0 were a nontrivial linear combination of First, we'll do addition. Let w and x be elements −1 of W . We have to show that vectors in T (S), then an application of T would yield a nontrivial linear combination of vectors in T −1(w + x) = T −1(w) + T −1(x): S, but there is none since S is independent. There- fore, T (S) is linear independent. We'll show that by simplifying it to logically equiv- For the second statement, let w be any vector in alent statements until we reach one which we know W , then T −1(w) is a linear combination of vectors is true. Since T and T −1 are inverse functions, that in V . Apply T to that linear combination to see equation holds if and only if that w is a linear combination of vectors in W . Since T carries both independent and spanning w + x = T (T −1(w) + T −1(x)): sets from V to W , it carries bases to bases. q.e.d. Since T is an isomorphism, we can rewrite that as More generally, any property of vector spaces de- fined in terms of the structure of vector spaces (ad- w + x = T (T −1(w)) + T (T −1(x)) dition and scalar multiplication) is preserved by iso- morphisms. which simplifies to w + x = w + x which is true. Scalar multiplication is left to you. Show Coordinates with respect to a basis deter- T −1(cw) = cT −1(w). q.e.d. mine an isomorphism. One of the main uses of a basis β = (b1; b2;:::; bn) for a vector space V We'll omit the proof of the next theorem. over a field is to impose coordinates on V . Each ' ' vector v in V is a unique linear combination of of Theorem 3. If S : V ! W and T : W ! X are the basis vectors both isomorphisms of vector spaces, then so is their composition (T ◦ S): V !' X. v = v1b1 + v2b2 + ··· + vnbn: 2 The coefficients are used as coordinates for v with the respect to the basis β 2 3 v1 6v 7 6 27 [v]β = 6 . 7 : 4 . 5 vn Let's denote the function that assigns these co- ordinates φβ. Theorem 6. The correspondence v to [v]β is an ' n isomorphism φβ : V ! F . To prove that theorem, you'll need to note that this is a bijection, prove that [u + v]β = [u]β + [v]β, and prove that [cv]β = c[v]β. Since the correspondence φβ is an isomorphism, it means we can work with coordinates with respect to a basis β of V just like ordinary coordinates. Corollary 7. Two finite dimensional vector spaces are isomorphic if and only if they have the same dimension. Proof. If they're isomorphic, then there's an iso- morphism T from one to the other, and it carries a basis of the first to a basis of the second. Therefore they have the same dimension. On the other hand, if they have the same dimen- sion n, then they're each isomorphic to F n, and therefore they're isomorphic to each other. q.e.d. Linear transformations. Next we'll look at lin- ear transformations of vector spaces. Whereas isomorphisms are bijections that pre- serve the algebraic structure, homomorphisms are simply functions that preserve the algebraic struc- ture. In the case of vector spaces, the term linear transformation is used in preference to homomor- phism. Math 130 Home Page at http://math.clarku.edu/~djoyce/ma130/ 3.
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